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Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Artificial neural network models predicting the leaf area index: a case study in pure even-aged Crimean pine forests from Turkey

作     者:ilker Ercanli Alkan Gunlu Muammer Senyurt Sedat Keles 

作者机构:Faculty of ForestryCankin Karatekin University18200 CankiriTurkey 

出 版 物:《Forest Ecosystems》 (森林生态系统(英文版))

年 卷 期:2018年第5卷第4期

页      面:400-411页

核心收录:

学科分类:0710[理学-生物学] 0830[工学-环境科学与工程(可授工学、理学、农学学位)] 0907[农学-林学] 09[农学] 0829[工学-林业工程] 0901[农学-作物学] 0833[工学-城乡规划学] 0713[理学-生态学] 0834[工学-风景园林学(可授工学、农学学位)] 

基  金:Funding from The Scientific and Technological Research Council of Turkey(Project No:2130026)is gratefully acknowledged 

主  题:Leaf area index Multivariate linear regression model Artificial neural network modeling Crimean pine Stand parameters 

摘      要:Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

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